Prof. Elizabeth Read
Department: Chemical & Biomolecular Engineering
University: University of California, Irvine
Lecture: Single-Cell Modeling (Bridging Stochastic Mechanistic Modeling and Statistical Inference to Shed Light on Epigenetic Regulation)
Date: June 14 @ 09:00 – 10:00
Abstract: In computational biology, mechanistic (or “bottom-up”) models start with hypotheses about molecular processes and then encode them into a mathematical and algorithmic framework for simulation of dynamics. In contrast, data-driven, “top-down” models generally apply statistical analyses with few mechanistic assumptions. I will discuss a current area of research where we seek to bridge these two approaches, in order to maximize knowledge gained from genomic data. I will discuss how stochastic models of enzymatic processes that confer epigenetic marks on DNA aid interpretation of local correlations derived from epigenome sequencing, with implications for epigenome stability.
Supplemental information:
- Stochastic modeling reveals kinetic heterogeneity in post-replication DNA methylation
- Locally-correlated kinetics of post-replication DNA methylation reveals processivity and region-specificity in DNA methylation maintenance